Internship in EEG & fNIRS Data Acquisition and (Pre-)Processing (f/m/x)

Motivation for the Work Turning today’s research into tomorrow’s applications – together. At ZEISS, we focus on user-centric innovation to transform ideas into cutting-edge solutions. The ZEISS Innovation Hub @ KIT fosters collaboration between students, researchers, and industry professionals to drive technological advancements. Your Role • Development of an efficient and reproducible workflow for the acquisition and preprocessing of EEG (electroencephalography) and fNIRS (functional near-infrared spectroscopy) data • Implement quantitative metrics to assess and optimize data quality • Curate and organize large datasets of stimulus-brain activity pairs for research applications • Establish online and offline methods for detecting and flagging bad recordings using visualization tools • Apply and evaluate advanced preprocessing techniques to increase the signal-to-noise ratio • Prepare data pipelines for AI and machine learning models (feature extraction, artifact removal, and normalization) • Collaborate with a team of engineers, neuroscientists, and AI researchers to integrate deep learning approaches into neural decoding • Present and discuss research findings in team and department meetings We Offer • A dynamic and interdisciplinary research environment • Exposure to state-of-the-art methods in neural signal processing and data curation • Opportunity to contribute to AI-ready datasets for machine learning applications for neural decoding • Close mentorship and the opportunity to continue your research as part of a master's thesis Your Profile • Enrolled in a bachelor’s or master’s degree program in biomedical/ electrical engineering, neuroscience, computer science, AI, or related fields • Strong programming skills in Python and NumPy • Solid understanding of electrical engineering principles • Basic knowledge of electrophysiology, neural signal processing, and machine learning • Experience with data preprocessing, signal analysis, and feature extraction is highly desirable • Familiarity with AI/ML concepts (e.g., supervised/unsupervised learning, deep learning architectures) is a plus • Creative, pragmatic, and self-motivated with strong analytical skills • Ability to work both independently and in a team-oriented environment • Excellent communication skills in English or German • Passion for innovation and enthusiasm for new technologies as well as motivation to work in agile, interdisciplinary teams Sounds exciting? Then become part of #teamZEISS and help us shape the future! Please provide your complete application documents (CV, transcript of records, etc.). Your ZEISS Recruiting Team: Selina Safradin